In these kinds of systems, the problem is to employ diverse capabilities to solve problems that are not only large but also multifaceted. As a simple example of distributed capability, we will use the example of distributed sensor network establishment for monitoring a large area for vehicle movements.

In this kind of problem, the overall task of monitoring cannot be done in a central location since the large area cannot be sensed from any single location. So the establishment problem is to decompose the larger monitoring task into subtasks that can be allocated appropriately to geographically distributed agents.

Distribution of capability, information, and expertise make no single agent solution to tasks possible. Solving distributed problems well demands both group coherence that is, agents need to want to work together and competence that is, agents need to know how to work together well. group coherence is hard to realize among individually-motivated agents.

In distributed problem solving, we typically assume a fair degree of coherence is already present: the agents have been designed to work together; or the payoffs to self-interested agents are only accrued through collective efforts; or social engineering has introduced disincentives for agent individualism; etc.

Distributed problem solving concentrates on competence; as anyone who has played on a team, worked on a group project, or performed on a football team can tell you, simply having the desire to work together by no means ensures a competent collective outcome.

Distributed problem solving presumes the existence of problems that need to be solved and expectations about what constitute solutions. For example, a problem to solve might be for a team of computational agents to design an artifact say, a car.

The solution they formulate must satisfy overall requirements it should have four wheels, the engine should fit within the engine compartment and be powerful enough to move the car, etc., and must exist in a particular form, a specification document for the assembly plant. The teamed agents formulate solutions by each tackling one or more subproblems and translation of subproblem solutions into overall solutions.

Sometimes the problem the agents are solving is to construct a plan. And often, even if the agents are solving other kinds of problems, they also have to solve how the agents should plan to work together— decompose problems into subproblems, allocate these subproblems, exchange sub-problem solutions, and synthesize overall solutions—is itself a problem the agents need to solve.

So distributed planning is tightly intertwined with distributed problem solving, being both a problem in itself and a means to solving a problem. One of the powerful motivations for distributed problem solving is that it is difficult to build training constructs to be competent in every possible task.

Moreover, even if it feasible to build or train capable agents, it is often overkill because, at any given time, most of those capabilities will go to waste. The strategy in human systems, and adopted in many distributed problem-solving systems, is to bring together on demand combinations of specialists in different areas to combine their expertise to solve problems that are beyond their individual capabilities.

There are several motivations for distributed problem solving and distributed planning. One obvious motivation is that using distributed resources concurrently can allow a speedup of problem solving thanks to parallelism. The possible improvements due to parallelism depend, of course, on the degree of parallelism inherent in a problem. One problem that permits a large amount of parallelism during planning is a classic problem in artificial intelligence.

The problem is to find a sequence of moves that will achieve the goal state. A second motivation for distributed problem solving and planning requires distributed expertise or other problem-solving capabilities.

For example, in concurrent engineering, a problem could involve designing and manufacturing an artifact such as a car by allowing specialised agents to individually formulate components and processes, and combining these into a collective solution. In application of flexible decision-making procedures to offensive attack war games thegoal of the predator agents is to coordinate their actions to capture one prey agent by surrounding it on all four sides.

Like anti-air defense domains, this domain also exhibits a trade-off between decision quality and computation time. While relying on reactive rules might degrade the quality of decision-making of a predator, relying on deliberative but delayed decisions might decrease the probability of capturing the prey as well.

The behaviours of predator agents using various strategies to surround the prey while closing in on it, can be compiled into reactive rules from a set of scenarios.

As in the anti-air defense domain, the performance proﬁles can be used to quantify the quality of decision-making depending on the number of other predator agents considered and on the depth of reasoning.

Based on performance proﬁles and the computed urgency of the situation, the predator agents can decide whether they should consider other agents or take the best current action without further deliberation.

Consider the example of agents delivering more than one newspaper. More than two newspapers are delivered by separate service agents using the phone.

The expenses of the agents depend only on the number of phone calls. There are several subscribers that subscribe to all the newspapers. All the delivery agents negotiate over the distribution of the common subscriptions.

Each of the agents can opt out of the negotiations and deliver all of its own newspapers by itself. The agents are compensated according to the time of the delivery. The faster the better.

We assume that a set of agents wants to satisfy a goal. All agents can take part in satisfying the goal, but they all need to agree on the schedule.

Schedules are inter-dependent with other agents. A schedule is valid if it satisﬁes both local constraints and also equality constraints with other agents.

For example, in meeting scheduling, a person has local constraints such as they can only attend one meeting at a time, but all attendees must agree on the time of a meeting, which is an equality constraint among the schedules of different agents.

Schedules are built incrementally. That is, new activities must be incorporated into an existing valid schedule to produce a new valid schedule. A key feature of incremental scheduling is that existing activities often need to be moved, or ”bumped” and rescheduled, in order to successfully accommodate the new activities.

Schedules contain private information and each agent retains ownership of its schedule. We assume this as an explicit property of the application domain.

This property eliminates a solution approach in which all information is communicated to a central scheduler that constructs a global schedule for all agents.

Instead, each agent makes its own scheduling decisions and communicates with others to ensure a valid schedule.

Importantly,the assumption of private information places limits on the information that is exchanged.All these are key essential features of many real-world distributed scheduling problems. Incremental scheduling is clearly an important class of problem.

Inter-dependencies as deﬁned by equality constraints arise whenever multiple agents must schedule a joint activity that must be executed at the same time, e.g., scheduling a coordinated invasion in military mission planning.

Design/Implement of extended group of partial planning coordination mechanisms to assist in schedule activities for teams of cooperative agents Involves Partial Planning Approach views coordination as fine-tuning local control, not replacing it. This process occurs via set of domain-independent coordination mechanisms posting constraints to local scheduler about the importance of defined tasks and appropriate times for task initiating and completion.

By concentrating on creation of local scheduling constraints, sequential scheduling is avoided in the original Partial Planning Approach occurring when there are multiple plans.

By having separate modules for coordination and local scheduling, we can take advantage of advances in real-time scheduling to produce cooperative distributed problem solving systems responding to real-time deadlines.

We can also take advantage of local schedulers with a great deal of domain scheduling experience already encoded in-house. Finally, our approach allows consideration of termination issues not typically addressed.

One motivation is that beliefs or other data can be distributed. For example, following the successful solution of the distributed sensor network establishment problem just described, the problem of actually doing the distributed vehicle monitoring could in principle be centralised: each of the distributed sensor agents could transmit raw data to a central site to be interpreted into a global view.

This centralised strategy, however, could involve tremendous amounts of unnecessary communication compared to allowing the separate sensor agents to formulate local interpretations that could then be transmitted selectively.

Another motivation is that the results of problem solving or planning might need to be distributed to be acted on by multiple agents. For example, in a task involving the delivery of objects between locations, distributed delivery agents can act in parallel.

The formation of the plans that they execute could be done at a centralised site, a dispatcher or could involve distributed problem- solving among them. Moreover, during the execution of their plans, features of the environment that were not known at planning time, or that unexpectedly change, can trigger changes in what the agents should do.

Again, all such decisions could be routed through a central coordinator, but for a variety of reasons exploiting parallelism, sporadic coordinator availability, slow communication channels, etc. it could be preferable for the agents to modify their plans unilaterally or with limited communication among them.

Note depending on the circumstances, different steps might be more or less difficult. For example, sometimes an overburdened agent begins with a bundle of separate tasks, so decomposition is unnecessary; sometimes the agent can pass tasks off to any of a number of identical agents, so allocation is trivial; and sometimes accomplishing the tasks does not yield any results that need to be synthesized in any complex way.

1. When an agent has many tasks to do, it should enlist the help of agents with few or no tasks. The main steps in task sharing are:

2. Task decomposition: Generate the set of tasks to potentially be passed to others.

3. Decomposing large tasks into subtasks that could be tackled by different agents. 4. Task allocation: Assign subtasks to appropriate agents.

5. Task accomplishment: The appropriate agents each accomplish their subtasks, which could include further decomposition and subtask assignment,

6. Recursively to the point that an agent can accomplish the task it is handed alone.

7. Result synthesis: When an agent accomplishes its subtask, it passes the result to the appropriate agent

8. Usually the original agent, since it knows the decomposition decisions

9. Is most likely to know how to compose the results into an overall solution.

10. When an agent has received solutions to all of the subproblems it passed down, it can compose these into a more comprehensive sequence of moves, and then pass this up as its solution.

Responsible Agents for Product-Process Integrated Design Project is developing agent-based tools using market place signals among members of a distributed design team to coordinate set-based design of discrete manufactured products.

Trade offs between industrial requirements and Multi-Agent System characterisation in design, implementation, and testing are described. Like any industrial project, it begins with requirements of the problem domain, and draws selectively from results of investigations to meet those requirements.

Distributed problem solving artificial intelligence facilitate agent cooperation work where distribution of capability, information, and expertise make no single agent solution to tasks possible.

Several motivations for application of distributed planning include using distributed resources concurrently to speed-up problem solving by agents thanks to determine what degree problem is characterised by parallel mechanisms.

Problem is to find sequence of moves with capacity to achieve the goal state. Another motivation for distributed problem solving and planning requires distributed agent expertise or other problem-solving capabilities.

In concurrent engineering, a problem could involve designing and manufacturing a vehicle by allowing specialised agents to individually formulate components and processes, and combining these into a collective solution.

Goal of distributed artificial intelligence is to develop mechanisms and methods that enable agents to interact better than workers and to understand interaction among agents. Key pattern of interaction in multi-agent systems is goal- and task-oriented coordination, both in cooperative and in competitive situations.

In the case of cooperation several agents try to combine their efforts to accomplish as a group what the individuals cannot, and in the case of competition several agents try to get what only some of them can have.

Engineers view what they do in terms of a life cycle, made up of a series of stages: requirements assess, design, implementation and deployment, operation, logistics and maintenance, and decommissioning. Any industrial activity follows such a pattern, whether it be building a product, putting in place the process for making a product, supplying a service, or creating a piece of infrastructure.

The entire life cycle of an industrial system is shaped by pressure to make a difference to the design team effectiveness. The complexity of real-world problems offers challenges every bit as stimulating as the more traditional challenges and the unforgiving nature of the business world provides a much clearer sense of success or failure than can be achieved in traditional domain constructs. The methods of designing, building, operating, and maintaining agent-based systems must be packaged if they are ever to find wide-spread deployment in the industrial world.

The life cycle perspective raises two questions about industrial multi-agent systems. First, to what stages in the life cycle of an industrial activity like making automobiles have agents been applied? Second, since an industrial agent-based system will itself be constructed according to a life cycle, what constraints does the industrial environment place on each of the life-cycle phases of such a system?

In this exposition, the term "project" represents a specific system or activity. The physical system divides at the design phase into two systems, one concerned with the product itself, the other concerned with the process that manufactures the product.

A generic life cycle has several phases, some of which may not be appropriate in a given project. Requirements Definition defines the set of needs or requirements that the project must satisfy. The focus is on why an effort is needed in the first place, not on what the project will do or how it will do it.

The life cycle for physical products bifurcates. Specification spells out the functions that the project will support. The specification tells what the project will do, but not how it does it. The functions in a successful specification will satisfy the needs identified in Requirements Definition and interface appropriately with other relevant components of the enterprise identified in Positioning.

A collection of shop-floor case studies highlights a set of issues that can explain the poor performance, including no way to schedule preventive maintenance leading to reduced maintenance and increased machine failure, operating policies that permit upstream workstations to produce parts for which there is no downstream demand, release of jobs to the floor before both raw materials and tooling are available, and job classifications that prevent operators from helping one another as demands shift across the factory.

Operation maintains the project in regular productive use. It is during this phase that the project actually satisfies the needs identified during Requirements Definition. Operation includes specific activities: routine operation, maintenance and repair, and incremental upgrading. to include customer support and maintenance.

Where in the Life Cycle Are Agents Used? In principle, agents can support many different stages in the life cycle of a system or product. For example, agents might help design a new vehicle, operate the plant that manufactures it, and maintain it when it fails. Agents have been used effectively: product design, process operation at the planning and scheduling level, and process operation at the lower level of real-time equipment control.

Agents in Product Design systems help teams of designers, often in different locations and working for different companies, to design the components and subsystems of a complex product, using many different assessment tools. As suppliers take increasing responsibility for the detailed design of the subsystems they supply, design becomes increasingly decentralised. Designers begin with a picture of what is required but no details on how it is to be produced.

The increased complexity embodied in modern products also favours the combinatorial benefits of an agent- based system. State-of-the-art agent concepts have been demonstrated in three design systems at the prototype level of maturity.

Each of these systems decomposes the world into agents in a different way, Agents can help human designers coordinate their work more effectively. Conflicts arise when different teams are responsible for the components and subsystems that make up a product. Who disagree on the relation between the characteristics of their own functional pieces and the characteristics of the entire product.

Some conflicts are within the design team: How much of a mechanism's total power budget should be available to the sensor circuitry, and how much to the actuator? Other conflicts set design against other manufacturing functions: How should one balance the functional desirability of an unusual machined shape against the increased manufacturing expense of creating that shape?

It is easy to represent how much a mechanism weighs or how much power it consumes, but there is seldom a disciplined way to trade off weight and power consumption against one another. The more characteristics are involved in a design compromise, the more difficult the trade-off becomes.

The problem is the classic dilemma of multivariate optimisation. Solutions are available only in specialised and limited niches. In current practice such trade-offs are sometimes supported by processes such as Quality Functional Deployment or resolved at the administration level, rather than in a way that optimises the overall design and its manufacturability. The problem is compounded when design teams are distributed across different companies.

Agents buy and sell the various characteristics of a design. Each characteristic agent is a computerised agent that maintains a marketplace in that characteristic. In the current implementation, the agents representing components are interfaces for human designers, who bid in these markets to buy and sell units of the characteristics.

A component that needs more latitude in a given characteristic like more weight can purchase increments of that characteristic from another component, but may need to sell another characteristic to raise resources for this purchase. In some cases, models of the dependencies between characteristics help designers estimate their relative costs, but even where such models are clumsy or nonexistent, prices set in the marketplace define the coupling among characteristics.

Set-based reasoning is used to drive the design process towards convergence. Most design in industry today follows a point-based approach, in which the participating designers repeatedly propose specific solutions to their component or subsystem. The chief engineer is expected to envision the final product at the outset, specifying to the designers what volume in design space it should occupy and challenging them to fit something into that space.

Some assumptions made by the chief engineer turn out to be wrong, requiring designers to reconsider previous decisions and compromise the original vision. This approach is analogous to constraint optimisation by backtracking. Because mechanisms for disciplined backtracking are not well developed in design methodology, this approach usually terminates through fatigue or the arrival of a critical market deadline, rather than through convergence to an optimal solution.

In set-based design, tasks of the chief engineer is not to guess the product location in design space, but to guide the design team in a process of progressively shrinking the design space until it collapses around the product. Each designer shrinks the space of options for one component in concert with the other members of the team, all the while communicating about their common dependencies.

This approach directly reflects consistency rules for solving constraint problems. If the communications among team members are managed appropriately, the shrinking design space drives the team to convergence.

Agents represent entities in the shop. represent manufacturing resources such as machines. These domain-oriented agents are clustered into communities, and each community has several service agents: a bidding agent that handles all transactions among domain agents, a constraint propagation agent that propagates task dependencies and does some constraint satisfaction, and a meta agent that registers the skills of the domain agents in the community.

Critical information in manufacturing is usually organised by physical entities. Agents that represent these entities are the locus for maintaining this information. Resource agents cache information regarding previous bidding and the utilisation of other compatible machines to guide subsequent bidding in directions that maximize overall goals and minimize later backtracking.

Resource agents store maintenance and reliability information, while part type agents model the supply and demand of their parts over time. Each domain agent has a friend module in which it caches information about its colleagues that it obtains in the course of interaction. Each agent also has access to information about its community indirectly through the meta agent, and directly through a community-wide blackboard. The community information includes both present state and future objectives.

Agents in Real-Time Control systems operate faster and with more constrained information than do planning and scheduling systems. They must provide real-time response. Current technology for industrial process control offers many examples of coordinated pro-active objects that can usefully be viewed as agent-based systems.

Physical sensors determine the state and location of the part, thus adapting their behaviour to what did or did not happen at earlier stations. Point-to-point non-persistent electronic communication between mechanisms guards against interference between mechanisms that may need access to the same physical space.

The preferred design for agent-based control is to maximise interaction through the physical environment and minimise such explicitly coded dependencies between mechanisms, because explicit linkages make systems susceptible to failure when one mechanism is modified.

Zone Logistics agents are assigned to specific physical mechanisms installed at fixed locations on the line, and so do not need to migrate over a network. Agent interaction in Zone Logistics is directive. Both sensor information and interference signals are conditions in agent rules that lead reactively to action. Reactive protocols are especially well suited to low-level control environments, in which the digital logic must keep pace with physical events in the real world. At higher levels of control, more complex protocols are useful.

Agents are assigned to mechanisms when the transfer line is constructed. Which agents are active on a given part depends on an electronic processing file that accompanies the part through the system. Logic agents coordinate their activity by propagating constraints. Market mechanisms are another candidate for real-time control.

How Does Industry Constrain the Life Cycle of an Agent-Based System? The industrial life cycle poses restrictions and constraints on developing an agent-based system that are not present in most research environments. Use cases deal more with the tools and techniques used in constructing agent-based systems and less with the characteristics of the agent-based systems themselves.

Conceptual Context Design of an engineered artifact such as an agent-based system is a process that takes place within a conceptual contest. In the agent research community, the "conceptual context" is often called an "agent architecture," and this subject has received considerable attention.

Relatively less attention has been paid to the important question of the processes that designers go through. Industrial users will use agents more readily if basic principles and guidelines are available in both areas. There is growing agreement among agent researchers on the set of issues that need to be resolved in order to design an agent-based system. Design must address both the individual agent and the community of which it is a part.

Naturally occurring agent systems have proven remarkably robust and adaptable, and suggest a set of useful engineering principles emphasis on precedents and practical benefits of physical rather than functional decomposition in agent-based systems. In most cases, deriving agents from the nouns in a narrative description of the problem to be solved yields things rather than functions. Legacy systems and watchdogs--agents that monitor the overall system for emergent behaviors are exceptions to this principle.

Broadly accepted standards bring users and developers together into a critical mass. If the requirements of various users differ widely from one another, developers will not have a large enough market for any single technology to justify the expense of bringing it to commercial status. If the offerings of different developers do not work together, users will not be able to assemble the full suite of tools that they require.

To the extent that agent standards agree with standards currently deployed in the pre-agent environment, they enable incremental introduction of agents, an approach that is less painful and more likely to be accepted by management than requiring a wholesale redesign of the factory to accommodate agents.

Sometimes it is not enough for agents to talk to one another over the network. If their interactions are intensive, they should share the same processor. A part agent may need to move from one machine agent to another during its residency in the shop. Applications enable agent behaviour to travel from one processor to another to provides a way for agents themselves to travel over networks and execute on diverse platforms.

These standards provide interoperability between different computer systems. Another category of standards enables people to communicate effectively with agents. Industrial engineers have evolved their own conventions for specifying and implementing systems, and they will accept agent technologies more readily if an agent system supports these conventions.

One of the great benefits of agent-based systems is their ability to generate complex system-level performance from relatively simple individual agents. This system level behaviour often cannot be predicted from the descriptions of individual agents, but must be observed in simulation or real-life.

As a result, the detailed behaviour of an implemented system may not be known in advance, and individual agent behaviours may need to be modified in real time as the system runs. The tools to support the monitoring and adjustment of an agent-based system in operation are the same ones needed to design the system in the first place so it is expected the more successful development tools will take on more and more features of operational interfaces.

1. How to enable agents to decompose their goals and tasks, to allocate sub-goals and sub-tasks to other agents, and to integrate partial results and solutions

2. How to enable agents to interact-- what communication languages and specification protocols to use

3. How to enable agents to represent and reason about the actions, plans, and knowledge of other agents to achieve interaction

4. How to enable agents to represent/reason about state of interaction processes to find out if progress in coordination efforts has been achieved

5. How to enable agents to recognise/reconcile disparate viewpoints and conflicts so accurate results are realized

The reason our “Digital Twin” Reports are so Special is that it is probably the only Guide you will find designed for General Officers who think they will never be able to understand what is a Digital Twin by themselves, feel like they wouldn’t like doing so anyway, but are still going to try. How do we know it will work for these reluctant leaders? Because it is written by a genuine, certified simple guy who has never been and will never be an engineer that finds it interesting and fun, at least at a basic level. If he can do it, so can you!

Virtual prototyping tools have already captivated DoD interest as a viable design tool. One of the key challenges is to extend the capabilities of Virtual Reality technology beyond its current scope of design reviews. Here we present the design and implementation of a Constraint Site Visit Executive Simulation designed to support interactive assembly and disassembly tasks within virtual space.

Smart block configurations can be instantly/securely sent and received reducing exposure/delays in rear echelons. As an example, oversight of Manoeuvre Requests could be securely implemented with greater transparency and also potential battlefield applications messaging system could be leveraged during instances in which troops are attempt to communicate back to HQ using secure, efficient and timely logistics system.

Most design engineers approach disciplines addressed one at a time before moving to the next one, and multiple iterations are performed through the design process in order to converge into a single solution. Each loop is a serial process that must be done in order, and control of each design variable must be carefully executed. The tech modules are highly coupled so that the dynamic process of integration is stable and converges on a solution.

But we have promoted an approach where discipline-specific designs are done in parallel across a broad design space. This process is designed to improve the flexibility of the design by delaying key decisions until the design space is fully understood, and the parallel approach also makes the process well fit for machine leaning application.

The direct relationship between the Sectional Construction Drawing, Planning and Sequence documents, and the Master Construction Schedule provides Job Site workforce with new tools to improve the logistics of a very complex process.

Capability of a Zone Logistics/Sectional Construction Drawing based plan must be progressed from the standpoint not only of cost but of overall schedule with far greater confidence that a conventional system structure component standup system drawing approach.

Zone logistics techniques are the focus for providing all the necessary requirements for constructing an interim product. Design products are brought into the conversation since the timely delivery of design products, that is, drawings, is particularly significant.

Key techniques employed by the Constraint Site Visit Executive Simulation are direct interaction, automatic constraint recognition, constraint satisfaction and constrained motion. Several optimisation techniques have been implemented to achieve real-time interaction with large industrial models.

Constraint-based approaches for virtual assembly simulations must be combined with physics-based investigations where geometric constraints are created or deleted within the virtual space at runtime. In addition, solutions are provided to low clearance assembly by utilising representation of complex models for accurate collision/physics results.

Constraint Site Visit Executive Simulation must also be able to validate recognised and applied constraints. The validation is the process that determines whether a constraint is still valid or is broken. A constraint is broken if the involved surfaces attempt to move apart beyond a defined threshold.

The goal of a lot of scientific and engineering activities has long been regarded the discovery of structural configuration. The design tasks in engineering sometimes need to combine the predefined components in order to obtain a desired configuration in a realistic time.

A predfined component is described by a set of properties, by a set of ports for connecting it to other components and by structural constraints . The configuration tasks select and arrange combinations of predefined components that satisfy all the requirements.

Configuration can be defined as special case of design activity feature product assembled from fixed set of pre-defined components connected in pre-defined ways. Selecting and arranging combinations of parts satisfying specifications is core function of configuration task.

Configuration comprises selection/instance parameters and composition of components out of pre–defined set of types so goal specification and set of constraints characterise domains

Configurable products are important in domains where standardised components are combined into customised products. A configuration task takes as input a model which describes the components that can be included in the product and a set of constraints that define how components can be combined, and requirements that specify properties of the product to be configured.

Output is a description of a product to be manufactured, a configuration. It consists of a set of components as well as a specification of how they interact to form the working product. The configuration has to satisfy the constraints in the model and the requirements.

In some configuration tasks optional components may be added or some components may require the existence of another component. This type of task leads to a constraint problem in which the set of variables that must be assigned a value may change in response to choices made in the course of problem solving. The solutions to such a problem differ in the sets of variables that are assigned values.

Constraint problems derived from design and configurations tasks often use components/structured values as domains of constrained variables. Most existing methods are forced into unnecessary search because they assign complete components to variables.

Partial choice is introduced as a way to assign a part of a component. The basic idea is to work with descriptions of classes of solutions as opposed to the actual solutions to reduce search and in the best case eliminate search. A distinction is made between a partial commitment ie, a partial choice that would not be retracted and a partial guess.

One technique utilised to implement partial choice problem solving organises choices into family classifications. Use of family organisation not only helps in paring down the search space but also provides a compact dispatch communication structure for describing solutions and representing constraints.

A product configurator has been an effective application tool in successful implementation of mass customisation strategy. It enables manufacturers to automatically generate product configuration information tailored to individual customer requirements.

But current product configurator techniques are not adequate to solve an engineering product configuration problem, because the constraints in such a problem are often expressed by mathematical formulae and computable procedures. This type of constraint posts challenges on constraint modelling and solving within the constraint satisfaction paradigm.

Constraints made up of computational for procedures are not naturally supported with pre-defined constraint semantics in a constraint model. It is also difficult to achieve search efficiency for constraints over continuous variables.

Here we present an innovative approach to modelling and solving an engineering product configuration problem based on the constraint satisfaction paradigm. It aims at developing methodology for a generic configurator that is able to solve an engineering product configuration problem with complex constraints.

The engineering design process can be considered to be constraint oriented. It involves the identification, negotiation and resolution of constantly changing set of constraints. Key characteristic of engineering design is that such problems are rarely as simple as satisfying a single objective with all the design variation continuous and unbounded.

As design factors develop, the designer can miss or overlook some of these constraints. To overcome this, there is a supportive approach which allows the designer to annotate the initial configuration design drawn models with the design constraints.

These constraints are then maintained with the model as it evolves, this presents the opportunity to refine the constraints when the design activity requires. The approach has been created to support manufacturing machinery design and is demonstrated with an industrial case study.

Constraints are imposed conditions, rules or limiting factors. Geometric and numeric constraints occur in engineering and computer-aided design, with applications in a number of mechanical design areas, including architectural drafting and robotics.

There is a clear difference between a geometric constraint and a numeric constraint. Simply put, a geometric constraint relates to other parts of a geometric figure, whereas a numeric constraint is a set number not relative to other parts of a design. Both geometric and numeric constraints define the dimensions of objects in computer-aided design modeling systems.

Geometric constraints define specific points on geometric objects and determine their orientations to other objects. Some examples of geometric constraints include parallelism, perpendicularity, concentricity and symmetry. Parallelism occurs when two or more lines or axes of curves are equidistant from each other. Perpendicularity is a constraint in which lines or axes of curves intersect at right angles. Concentricity arises when two or more arcs, circles or ellipses share the same center point. Symmetry occurs when selected lines or curves become symmetrically constrained around a selected line. configuration design drawn method called "geometric constraint solving" involves finding the configurations of lines, points, circles, and other geometric figures that are constrained to have established relationships to each other.

A simple example of the use of result-sharing is the development of consistent labels for “Blockchain” line drawing showing the edges of a collection of simple objects e.g., cubes, wedges, and pyramids in a scene.

Each image is represented as a graph with nodes that correspond to the vertices of the objects in the image and arcs that correspond to the edges that connect the vertices. The goal is to establish a correspondence between nodes and arcs in the graph and actual objects.

Your ability to deal with complex changing structures means that computers can now be applied to direct systems such as networks of trading partners that formerly required extensive manual attention. Increased directive complexity also extends the scope of operational applied approach problems.

Using the auto configuration design drawing application, you can constrain two geometric objects by performing certain commands. For example, you can select a location on a figure and then select a location on another figure that will move toward the first until those selected points coincide.

Until you remove the constraint, these objects will continue to have this relationship. If you use a command on a constrained object, it affects the other objects that depend on the constraint. For example, when you constrain two objects to be symmetrical, rotating the line of symmetry will rotate the constrained objects as well. The "Auto Constrain" feature in auto configuration design drawing applies a set of constraints automatically, depending on your choice of objects.

Configuration design drawing is design program that uses constraints. "Constraint Manager," feature which automates the process of working with different types of constraints. It allows you to manipulate the spatial relationship between objects and develop predictive outcomes during the design modification process.

Using constraints properly is an effective, time-saving technique as you execute your design. There are comprehensive tutorials available, both online and offline, providing step-by-step instructions on how to use configuration design drawing "Constraint Manager."

“Constraint Manager” Simulation identifies new possible constraints and validates existing ones. The application specifies a list of objects to be searched for new constraints and possibly the surfaces to be tested for new constraints. If the application can determine collisions between surfaces, it can send those colliding surfaces to “Constraint Manager” Simulation. This speeds up the recognition process because it cuts the number of surfaces to be tested.

As the technology has advanced capability of applications has improved dramatically, allowing designers and engineers to manipulate objects on a screen in 3D and make infinite modifications that in turn are quickly translated into code automatically. This has greatly sped up the process of machining, allowing operators of even limited experience the ability to successfully create acceptable finished parts.

Technically “Constraint Manager” doesn’t need to know code. If the configuration design drawing has already created a cutting program, it feeds that information to the machining centre. Applications have already determined the “speed and feed”, the tool path and all the other variables needed to make the part. The operator can simply press the start button and watch the part being made, but there are some problems to this approach

Configuration design drawing programmes do not always produce the optimal tool path for the fastest and most efficient cutting of a part, especially for complex geometries. This is because, as mentioned above, it is learning point-by-point and step-by-step, not taking the entire picture into account.

Only a “Constraint Manager” with real-world experience is capable of determining the ideal function of the machine tool to meet the expectations of customer design intent. configuration design drawings are also optimised for maximum safety and machine tool life, which translates as slow. Sometimes very slow.

Also, configuration design drawning applications can sometimes make errors, or create a cutting programme that may need to be tweaked. If an operator doesn’t know how to modify individual lines of code they would then need to programme the job again from scratch, wasting valuable time, but the experienced operator can fine tune the program, one line of code at a time, to create the most efficient program to create the highest quality part with the lowest cycle time.

Let’s look at how we optimise machine programming to make parts faster while improving quality and consistency. code is the generic industry term for the computer language that most assembly machines use to control their movements and how they make parts.

Code is created as the output from advanced configuration design drawing aided design/computer aided manufacturing applications. Since there are many different brands of design applications available the type of code they generate will also be different. However, most major brands have translation ability that allows them to be compatible with the vast majority of commercially available machines.

Each line of code tells the machine to perform one discrete action, including position, speed, rotation, etc. Shapes are made by stringing together point-by-point sets of instructions. Even simple parts can require hundreds or thousands of lines of code, and ultimately they must all work perfectly together to achieve the desired result.

Because design code works as a series of points and line-by-line instructions, it is possible to make the same part using a variety of paths and instructions, and the resulting parts are not always the same.

We specialise in rapid tooling, rapid prototyping and fast turnaround low-volume production, we deal with a steady influx of new designs every day. That means we must be experts at a number of techniques, and we also must create new cutting programmes all the time. Our operators are proficient in using code and they’re mentored by master machinists with decades of manual and experience.

We know how to get the most out of our advanced equipment, eliminating downtime while working to tight tolerances that are repeatable, time after time. Contact one of our customer service engineers to find out how our team can optimise the making of your next project.1. Explain forward checking on basis of simple example

“Digital Twin” is one of the top strategic enterprise trends today. Digital Twin Builder system is designed to autonomously build digital twins directly from streaming data in the edge mission space. The system is built for the emerging AI network world in which real-world devices are not just interconnected, but also offer digital representations of themselves, which can be automatically created from, and continually updated by, data from their real-world counterparts.

Builder addresses these challenges by enabling any organisation with lots of data to create digital twins that learn from the real world continuously, and to do so easily, affordably, and automatically.

Digital twins are digital representations of a real-world object, entity, or system, and are created either purely in data or as 3D representations of their physical counterparts. For example, every component of large machines can be stored as a digital twin in the Builder system.

This allows engineers only know where everything is and what it looks like, but also how well components are performing and when they need upgrade, repair, or replacement. But for most organisations that kind of massive, programme isn't an option. They need something simpler, easier to deploy, and cheaper.

Digital Twin Builder system is designed to enable digital twins to assess, learn, and predict their future states from their own real-world data. In this way, systems can use their own behaviour to train accurate behaviour models. The important difference to other AI solutions is this ability is offered as a service in real-time, without centralised, batch-oriented big-data analysis.

Key challenges include how enterprises can implement the technology, given their investments in legacy assets. Limited skill sets in streaming analytics, coupled with an often poor understanding of the assets that generate data within complex AI network systems, make deploying digital twins too complex for some. Meanwhile, the prohibitive cost of some digital twin infrastructures puts other organisations off.

Digital Twins need to be created based on detailed understanding of how the assets they represent perform, and they need to be paired with their real-world pairs to be useful to stakeholders on the front line. "Who will operate and manage digital twins? Where will the supporting infrastructure run? How can digital twins be joined up with AI networks and other applications, and how can the technology be made useful for agile business decisions?"

The ability to add AI at the edge is an increasingly important element for networks as companies look to improve data processing and efficiency. Digital twins are digital representations of a real-world object, entity, or system, and can be enhanced with AI networks. Improvements can be made to digital twin technology to help AI networks.

Robots are replacing somemotor-drive-gear-operated systems. The robots perform the same processes, for example, cutting, bending, and sealing tasks for a shipping box, but their arms are driven by independently controlled servomotors. “The old machines are much cheaper to build, but they can only do one thing. So, if you had a new package, you either had to re-engineer your machine or scrap it. With robots, it’s simply a matter of changing the packaging profiles and the application parameters.”

Still, reprogramming robots takes time. In the past, an engineer might have used applications to simulate how the line would handle the new packaging, but then have to pull those robots off the line. Then the engineer would test and tweak the movement of materials through the machine and the ability of the robotic arms to make and package containers, all the while trying to shave time off each cycle.

A digital twin goes one step beyond conventional models. It models the robotic line with such high fidelity, the engineer can do all this in the virtual world. After adjusting the model profiles and operating parameters, the engineer simply exports the profiles and parameters to the control system of the physical equipment. So it should run perfectly the first time. “That alone will certainly disrupt the packaging machine industry,” “That’s the future, and it’s certainly where digital twins for large machines will end up.”

Yet this is clearly not all digital twin technology. is capable of. In fact, simulating individual machines is only the beginning. Because the real power of a digital twin is not that it optimises a single machine, but that it interacts with the digital twins of every piece of equipment in a factory and the digital twin of every product those machines make.

And it its not limited to optimising those production processes in the virtual world. Digital twins run in tandem with their highly instrumented physical twins, fed by data by from actual operations. By comparing the output of the digital and physical systems, engineers can quickly spot problems before they arise, avoid bottlenecks, and find new ways to boost throughput and reduce costs.

In short, digital twins are the foundation of tomorrow’s smarter workplace.

Digital twins may seem like the newest buzzword but the concept dates back two decades. Their premise was simple: A digitally modeled system is really composed of two systems, a physical system and a virtual system that contains all the information about the physical system. They can exist for products and for processes.

Engineers have used product models for decades, but only recently have they achieved the extraordinary fidelity needed for digital twins. “In industries like automotive, we can define a big chunk of our products geometrically such that it is almost impossible to determine whether a representation is virtual or physical. Product models slash development time by letting engineers build and test virtual prototypes to optimise design and cost. Now the same approach is used in manufacturing processes..

Conceptually, it is not much of a jump from testing product designs to simulating manufacturing processes. In fact, many application programs do something similar today. What makes digital twins different is their fidelity and their ability to handle large amounts of data in real time.

Even modest factories are complex, and they have far fewer constraints than any complex product designed to operate in a specific way. In a factory, operating procedures are always changing. Even a simple drill press might bore aluminum one day, then switch bits, speed, and coolant for steel the next. A modern factory might make many products, and the flow of materials from machines through assembly stations will change with them.

The digital twin of a factory must be robust enough to capture those changes, plus all relevant data from each operation. That takes massive AI horsepower. Fortunately, networks have grown more powerful and manufacturers can now tap the cloud to store and analyze factory data using cognitive computing programmes.

Modeling tools have also advanced, especially their ability to generate “lightweight” models. “We can select the geometry, characteristics, and attributes we require without carrying around unnecessary details,. This dramatically reduces the size of the models and allows for faster processing.”

Reducing data requirements lets digital twins visualise and simulate complex systems without drowning in a flood of extraneous real-time data. It takes highly instrumented equipment to supply that data. While manufacturers have been adding sensors to the shop floor for decades, digital networks are making it cheaper and easier to collect up-to-the-minute factory data for performance analysis.

New design and production sites are based on digital twins and the AI/Cloud data needed to feed those models. “Plants are always changing, people are moving around, machines break and lines slow down. The digital twin will only work if it reflects the reality of the shop floor.”

Digital product models contain each component that goes into a product, from screws and welds to plastic shapes and machined metals. The digital twins that drive a factory have an associated bill of process for each of those components. This “instruction manual” describes the steps needed to produce and assemble those components into the final product. Product and process twins work together.

“The digital twin can provide the manufacturing execution system with step-by-step instructions for making that product. Builder system can reference that instruction manual and perform all the coordination tasks to guide the product through the factory, setting up machines on the fly, and check that each step is done correctly.”

The twins let engineers test-drive new processes. They could, for example, add a new machine to their virtual line and see how it affects output of specific products, or test whether relocating equipment or readjusting workflow between machines improves output. The result is not just an optimised machine, but an optimised process. It will be possible to do laser-scanning on an entire factory to model its infrastructure, then drop digital twins of machinery and logistics systems into it.

Builder does thousands of virtual production runs to see if product is designed with manufacturing in mind. Simulation can be run where workers put on gloves and see if they can still assemble the product, or create assembly cells that combine collaborative robots and people and see if that helps.

Once the physical line is up and running, its sensors will send operating and inspection data to the factory digital twins. These models provide a detailed view of factory operations. By looking for unexpected variances between actual and simulated data, engineers can probe for potential problems that might reduce operating rates or quality.

Digital twins support greater automation. As orders come in, the system will make sure the proper parts are in inventory, schedule machine time, and route components from workstation to workstation with only minimal human intervention. Each step of the way, the plant autonomously checks product and machine specs against their digital twins to ensure each operation is carried out correctly and that no equipment is drifting out of tolerance.

“Digital Twin” fully connected factory linked with internet sensors and cloud analytics is still a work in progress. Yet this has not stopped engineers and manufacturers from simulating some operations with existing tools. One team created a virtual model of the production line that would build its new vehicle while still designing the car digitally.

This interplay between product and process digital twins ensured the factory could produce and assemble the parts its designers had envisioned. It also helped work out problems before production. When manufacturers offer highly customised products, the virtual factory had to be flexible enough to create the parts needed for each combination without slowing down.

To be able to introduce the new models to the market as quickly as possible, engineers laid out the new lines while the vehicle was still on the drawing board. The Design engineers rapidly went through different modification scenarios of the new models over and over again.

Accordingly, the production facilities needed continuous adjustments . Fortunately, application tools are rapidly rising to the challenge of concurrently building and integrating digital twins. Builder analyzes how vehicle design changes affected production, so it shows where to focus their attention. As the technology evolves, those tools will grow more powerful and be able to handle more complexity.

AI enabled digital twins will become more tightly integrated into plant production processes, and far more capable. They will also become smarter, using machine learning programs, a type of artificial intelligence, to learn more about factory machines and improve the ability of digital twins to simulate and predict their behaviour.

“At the outset, there is a good idea of what operating parameters should be, but it is key to improve prediction capabilities by incorporating data as the machine is operating, and learn from that data.

As AI systems learn more about specific machines, they will use their digital twins to help engineers run plants more efficiently. A suspect sound coming from a machine? AI can analyze it to see if a screw is loose or a bearing is starting to fail. The better the AI knows the machine, the more accurately it can predict when that failure is likely to happen. And the more options—fix it now, run the machine to maintenance, or readjust production schedules and take the machine offline—it can offer a plant administrator.

Digital twins are evolving rapidly. Where will it end up? More economical manufacturing of small lots, or even lots of one? Maybe. Hyper-customised products? Perhaps. Fully programmed and optimised production lines that need only a few hours of shakedown before startup? That would be great. Machines managing and controlling other machines? Closer than we think. That is what a demo is used for to automate machinery with extensive digital product and process twins to keep everything on track. The results are stunning. By using digital instruction manuals and robots to move parts from one workstation to the next, it can produce products very quickly.

Digital twin simulations can be compared to physical machine and product data, so the factory can tune and retune its equipment. This achieves remarkable levels of quality: despite churning out huge lots of different products every day. This is very much what mass customisation looks like. Eventually plants may churn out customised products nearly as inexpensively as factories that make mass-produced models.

Digital twins will make that possible, as well as a whole lot more. Their future is still being written.

But difficult as it may be to accept, sometimes “Digital Twin” terminology can get in the way of innovation. A case in point is the “model-based” cluster of engineering design, manufacturing, and enterprise terminologies and methodologies.

While “Digital Twin” implementation can be beneficial, redundancies and overlaps foster long-running confusion in both traditional worker and digital context.

In recent years, many similar difficulties have been overcome with “Digital Twin” lifecycle management strategies reshaping how many enterprises handle their data, i.e., integrated from initial concept of a product to the end of the product life and often beyond that with nothing relevant left out. As digitalisation continues its rapid penetration of enterprises, the overall economy, and DoD transformation is all around us.

This “Digital Twin” transformation and its many innovations depend on visibility, connectivity, and traceability of data whether structured or unstructured. Significant parts of this transformation are being compromised by implementations with closed-system architectures and limited connectivity.

Overlaps and the confusion they foster tend to isolate capabilities from innovative processes and workflows that are required by the enterprise. The problem is routinely encountered in the aerospace and defense industries.

When one steps back a bit, it is as if “Digital Twin” terminologies and methodologies are fighting each other for dominance, market space, and behaviour. Given their history and how they are managed and mismanaged, this shouldn’t be a surprise. That this situation persists, however, is a surprise.

In short, it's time to clean up “Digital Twin” terminology and to take fuller advantage of is required end-to-end enablement-- challenging developers, marketers, and standards committees to sort out terminology and agree on common-sense definitions with minimal overlaps.

DoD must begin with recognising that there is a problem and that it can be fixed —that logical, everyday definitions of can be agreed on by all concerned. Until this is achieved, fundamental difficulties in enabling good practices will not be solved.

When viable “Digital Twin” terminology agreements are reached, benefits can be expected quickly: The all-important exchange of information between the factory floor and design engineers will be simplified and sped up.

Better connectivity will make workflows more visible across the enterprise, so they can be leveraged at many points in the lifecycle. With connectivity and visibility comes greater transparency of processes and potential for capabilities to become widely available across the enterprise and in the extended enterprise of partners, suppliers, and customers.

“Digital Twin” solution providers will put developers to work on better-enabled framework. To grasp these potential benefits, it helps to look into the generally accepted terminologies:

Collectively known as product manufacturing information, these data sets and their linked repositories contain the information to manufacture and inspect the product. Model-Based Definitions are also known as “digital product definitions.”

“Digital Twin” Model-Based Design is a mathematical and visual method of addressing problems in designing complex control, signal processing and communication systems. In Model-Based Design, models are developed in simulation tools for rapid prototyping, testing, verification, predictive signals, and record libraries.

Model-Based Design is also a communication framework to represent shape, behavioural, and contextual information throughout the design process and development cycle.

Model-Based Engineering is the use of models as the authoritative definition of a product or system's baseline technical details. Intended to be shared by everyone involved in a project, these models are integrated across full lifecycles and span all technical disciplines.

Model-Based Systems Engineering is a methodology to support system requirements, verification, and validation activities from conceptual design, throughout development and on into later lifecycle phases. Like other “Digital Twin” components, engineers use these simulations to exchange information.

Model-Based Enterprise refers to an organisational work space that leverages the model as a dynamic artifact in product development and decision-making. The Model-Based Enterprise focuses on the management of lifecycle feedback to create follow-on products and their iterations and variants.

Integration considerations always bring us back to DoD specification standards and, as we have seen, they are a big part of the problem. There are at many separate groups developing standards relevant to “Digital Twins“, which impact it or join it to related processes and information-constructs.

None of these standards is adhered to by all developers and solution providers. Practice workshop attendees noted that each solution provider defines “Digital Twin” in ways that best align with its marketplace stance and competitive advantage, i.e., they define “Digial Twin” to be what their solution can do. So it is useful to use cross-discipline standards that adhere to end-to-end, “system of systems” approaches.

There is another dimension to the “Digital Twin” problem: the difficulty of implementation .

It is time to fold “Digital Twin” methodologies and associated terminologies together to fully integrate them into enterprise information infrastructures. Only then can tech be enabled with the lifecycle data and process management it requires. When this is done, one of the goals of digitalisation will be realised.

Product Innovation Platforms enable “Digital Twin” to optimise everything from customer requirements, product behaviour and performance metrics using sensors through end of service life . Product Innovation Platforms let users collaborate and innovate more effectively with seamless and transparent data sharing throughout the entire lifecycle.

Modern DoD demands for data sharing require that integration be reliable, which is a core value fo “Digital Twin“. Must have capability to manage new-product data across billion-dollar operations—gathering, preserving, and updates cannot be used across the enterprise without “Digital Twin.”

For the good of innovative product development and the beefing up of enterprise competitiveness, we believe it is time for everyone responsible for “Digital Twin” to take an unbiased look at the gains to be won from implementation key part of digitalisation and the sweeping transformations accompanying digitalisation, all of which are inevitable.

Unless and until agreement is reached on the need for change in “Digital Twin” methodologies and associated terminology, they will continue to be a stumbling block. Knowledgeable experts from industries using “Digital Twin” must cross traditional engineering boundaries and integrate team work orders. Benefits of using Digital Twins include:

1. Helps you move validation processes into the virtual world – but still keep you connected to how your products act in the physical world. This virtual-physical connection lets you determine how a product performs under a number of conditions and make necessary adjustments in the virtual world to ensure the physical product will perform exactly as planned in the field, reducing risk. Digital twins help you navigate world of complex systems and materials to make best possible decisions with confidence.

2. Helps you validate how your production process will act on the shop floor before anything actually goes into production. By perfecting this performance using your digital twins, and understanding why things are happening using the digital thread, your prevent costly downtime to machines and robots on the shop floor. You can even predict when maintenance will be necessary to avoid unnecessary downtime.

3. Helps you save time and money in simulation, testing and assessments so you no longer have to rely on only physical constructs; instead, you can include information from physical performance in your digital twins to maintain a high version of fidelity and reality in the virtual world. This constant stream of accurate, updated information gives you the situational awareness you need to make decisions faster, increase your production speed and optimise your productivity to get to market faster.

4. Helps you develop intelligence to feed advancements and reduce risks in the future products. Machine data collected over a period of time can enable digital prototypes to sustain life of product and help human operators make better decisions to enhance product performance

5. Helps you generate production data in real time to reflect the current and future performance status of their physical counterparts to enable sharing with technicians or other interested groups remotely. Virtual representations will be able to predict faults and errors and help avoid costly implications.

6. Helps you generate value in the form of less maintenance costs, new revenue streams, and better management of assets. As technology improves and virtualisation options become more pronounced, you will be able to deploy digital twins with even less capital investment while deriving greater returns on investments in a shorter time period.

7. Helps you be able to provide an integrated outlook of any project, to any user, at any point of the product lifetime. This single source of validated information allows you to foster collaboration across various teams and departments, and even outside the organisation. Engineers can simulate the behaviour of complex systems to predict and prevent mechanical breakdowns.

8. Helps you advance existing processes, products and services and often lead to new market opportunities while significantly cutting operating costs, leading to real bottom-line improvements. Digital twins help you propel traditional manufacturing to a new competitive level via intelligent connected products.

9.Helps you improve product performance while mitigating both the cost and risk of a new product introduction. Digital twins can dramatically speed product realisation time as you decrease or eliminate the most time-consuming aspects of building products in the real world. Early discovery of system performance deficiencies uncovered by simulating results before physical processes and product are developed compresses time to value relationship

10. Helps you demonstrate product value proposition before build stage with opportunity to link organisational tools, skills and knowledge bases. It’s becoming increasingly apparent that different manufacturing concepts and methodologies are required to take a product idea to commercialisation. Continuous refinement of design models is possible through data captured and easily crossed referenced to design details.